




"""simple docstring"""







import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow

@require_torch
class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	'''ylacombe/bark-small'''
       UpperCamelCase              =	tempfile.mkdtemp()
       UpperCamelCase              =	'''en_speaker_1'''
       UpperCamelCase              =	'''This is a test string'''
       UpperCamelCase              =	'''speaker_embeddings_path.json'''
       UpperCamelCase              =	'''speaker_embeddings'''







   def   UpperCAmelCase__			(		self       ,					**lowerCamelCase_)				->   str:
       return AutoTokenizer.from_pretrained(self.checkpoint       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Any:
       shutil.rmtree(self.tmpdirname)







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	self.get_tokenizer()

       UpperCamelCase              =	BarkProcessor(tokenizer=lowerCamelCase_)

       processor.save_pretrained(self.tmpdirname)
       UpperCamelCase              =	BarkProcessor.from_pretrained(self.tmpdirname)

       self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer.get_vocab())







   @slow
   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint       ,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,					)
       processor.save_pretrained(
           self.tmpdirname       ,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,					speaker_embeddings_directory=self.speaker_embeddings_directory       ,					)

       UpperCamelCase              =	self.get_tokenizer(bos_token='''(BOS)'''       ,					eos_token='''(EOS)''')

       UpperCamelCase              =	BarkProcessor.from_pretrained(
           self.tmpdirname       ,					self.speaker_embeddings_dict_path       ,					bos_token='''(BOS)'''       ,					eos_token='''(EOS)'''       ,					)

       self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer_add_kwargs.get_vocab())







   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint       ,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,					)

       UpperCamelCase              =	3_5
       UpperCamelCase              =	2
       UpperCamelCase              =	8

       UpperCamelCase              =	{
           '''semantic_prompt''': np.ones(lowerCamelCase_),
           '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)),
           '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)),
       }

       # test providing already loaded voice_preset
       UpperCamelCase              =	processor(text=self.input_string       ,					voice_preset=lowerCamelCase_)

       UpperCamelCase              =	inputs['''history_prompt''']
       for key in voice_preset:
           self.assertListEqual(voice_preset[key].tolist()       ,					processed_voice_preset.get(lowerCamelCase_       ,					np.array([])).tolist())

       # test loading voice preset from npz file
       UpperCamelCase              =	os.path.join(self.tmpdirname       ,					'''file.npz''')
       np.savez(lowerCamelCase_       ,					**lowerCamelCase_)
       UpperCamelCase              =	processor(text=self.input_string       ,					voice_preset=lowerCamelCase_)
       UpperCamelCase              =	inputs['''history_prompt''']

       for key in voice_preset:
           self.assertListEqual(voice_preset[key].tolist()       ,					processed_voice_preset.get(lowerCamelCase_       ,					np.array([])).tolist())

       # test loading voice preset from the hub
       UpperCamelCase              =	processor(text=self.input_string       ,					voice_preset=self.voice_preset)







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	self.get_tokenizer()

       UpperCamelCase              =	BarkProcessor(tokenizer=lowerCamelCase_)

       UpperCamelCase              =	processor(text=self.input_string)

       UpperCamelCase              =	tokenizer(
           self.input_string       ,					padding='''max_length'''       ,					max_length=2_5_6       ,					add_special_tokens=lowerCamelCase_       ,					return_attention_mask=lowerCamelCase_       ,					return_token_type_ids=lowerCamelCase_       ,					)

       for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]       ,					encoded_processor[key].squeeze().tolist())

from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

